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Absolute stability and dissipativity of continuous time multilayer recurrent neural networks

Absolute stability and dissipativity of continuous time mult...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： J.A.K. Suykens J. Vandewalle Department of Electrical Engineering ESAT-SISTA Katholieke Universiteit Leuven Leuven Belgium

In this paper we present a sufficient condition for global asymptotic stability of continuous time multilayer recurrent neural networks with two-hidden layers. The condition is based on a Lur'e-Postnikov Lyapunov function and is expressed as a matrix inequality. With respect to input/output stability a condition for dissipativity is derived, which includes, for example, the cases of passivity and finite L/sub 2/-gain. This result is based on a quadratic storage function plus integral term. For nonlinear modelling and control purposes it enables to modify the classical dynamical backpropagation algorithm with a matrix inequality constraint in order to guarantee stable identified models or stable closed-loop control schemes, in a similar fashion has this can be done in discrete time NL/sub q/ theory.

关键词： Multi-layer neural network Recurrent neural networks Asymptotic stability Linear matrix inequalities Lyapunov method backpropagation algorithms Control theory Stability criteria Sufficient conditions Neural networks

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Hebbian learning with winner take all for spiking neural networks

Hebbian learning with winner take all for spiking neural net...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ankur Gupta Lyle N. Long Department of Computer Science and Engineering Pennsylvania State University University Park PA USA Aerospace Engineering Bioengineering and Mathematics Pennsylvania State University University Park PA USA

Learning methods for spiking neural networks are not as well developed as the traditional rate based networks, which widely use the back-propagation learning algorithm. We propose and implement an efficient Hebbian learning method with homeostasis for a network of spiking neurons. Similar to STDP, timing between spikes is used for synaptic modification. Homeostasis ensures that the synaptic weights are bounded and the learning is stable. The winner take all mechanism is also implemented to promote competitive learning among output neurons. We have implemented this method in a C++ object oriented code (called CSpike). We have tested the code on four images of Gabor filters and found bell-shaped tuning curves using 36 test set images of Gabor filters in different orientations. These bell-shapes curves are similar to those experimentally observed in the V1 and MT/V5 area of the mammalian brain.

关键词： Hebbian theory Neural networks Neurons Biological neural networks Object oriented modeling Biological information theory backpropagation algorithms Learning systems Artificial neural networks Timing

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Multilayer recurrent neural networks [character recognition application example]

Multilayer recurrent neural networks [character recognition ...

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： H.K. Kwan Department of Electrical and Computer Engineering University of Windsor Windsor ONT Canada

In this paper, multilayer recurrent neural networks in the form of 3-layer bidirectional symmetrical and asymmetrical associative memories are presented. The networks possess the features of both a multilayer feedforward neural network and a bidirectional associative memory. These networks can have two modes of recalling; namely, recalling by one pattern and recalling by a pattern pair. Simulation results on alpha numeral type pattern recognition performance under noisy conditions are given.

关键词： Recurrent neural networks Multi-layer neural network Associative memory Neural networks Neurons Pattern recognition Magnesium compounds backpropagation algorithms Feedforward neural networks Hebbian theory

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Hybrid speech recognition system with discriminative training applied for Romanian language

Hybrid speech recognition system with discriminative trainin...

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Mediterranean Electrotechnical Conference (MELECON)

作者： I. Gavat M. Zirra O. Cula Polytechnic University of Bucharest Bucharest Romania ADtech Advanced Technology Bucharest Romania Polytechnic University of Bucharest Romania

This paper describes a hybrid connectionist-statistical system consisting of a neural network integrated in a hidden Markov model (HMM). The neural network used is the multilayer perceptron (MLP) and that network is the mechanism that computes the a-posteriori probability of a sequence of HMMs states. The classifier is based on total scores computed by Viterbi alignment for each hybrid model corresponding to the words in the vocabulary. Because of the lack of discrimination between the models and the unintended discrimination between the states in each model, we propose a solution that improves the system, namely an additional training task based on a cost function that approximates the misclassification rate of the hybrid system. The optimization criterion is based on a descent algorithm and the result is a minimum classification error. Our experiments on a 35 word vocabulary, show an improvement of the recognition rate from 92.4% for the case of a statistical system based only on HMMs, to 94.7% for the case of a hybrid HMM-MLP system, and to 97.9% for the case of an improved hybrid system with an extra discriminative training.

关键词： Speech recognition Natural languages Hidden Markov models Power system modeling Artificial neural networks Neural networks Viterbi algorithm Vocabulary Speech processing backpropagation algorithms

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Diagonal recurrent neural network based control using adaptive learning rates

Diagonal recurrent neural network based control using adapti...

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IEEE Conference on Decision and Control

作者： C.-C. Ku K.Y. Lee Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A method of choosing learning rates adaptively in controlling a dynamic system using diagonal recurrent neural networks is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself and not to other neurons in the hidden layer. An unknown plant is identified by a diagonal recurrent neuroidentifier (DRNI), and provides the sensitivity information of the plant to a diagonal recurrent neurocontroller (DRNC). A dynamic backpropagation algorithm (DBP) is used to train both DRNC and DRNI. The DRNN captures the dynamic nature of a system, and, since it is not fully connected, training is much faster than for the case of a fully connected recurrent neural network. For faster learning, the use of an adaptive learning rate that guarantees convergence is developed. The proposed approach is applied to numerical problems. Simulation results are included.< >

关键词： Recurrent neural networks Programmable control Adaptive control Neurons Control systems Neurocontrollers backpropagation algorithms Convergence

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Small business credit scoring: a comparison of logistic regression, neural network, and decision tree models

Small business credit scoring: a comparison of logistic regr...

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International Conference on Information Technology Interfaces

作者： M. Zekic-Susac N. Sarlija M. Bensic University of Josip Juraj Strossmayer in Osijek Osijek Croatia Department of Mathematics University of Josip Juraj Strossmayer in Osijek Osijek Croatia

The paper compares the models for small business credit scoring developed by logistic regression, neural networks, and CART decision trees on a Croatian bank dataset. The models obtained by all three methodologies were estimated; then validated on the same hold-out sample, and their performance is compared. There is an evident significant difference among the best neural network model, decision tree model, and logistic regression model. The most successful neural network model was obtained by the probabilistic algorithm. The best model extracted the most important features for small business credit scoring from the observed data

关键词： Logistics Regression tree analysis Neural networks Decision trees Linear discriminant analysis Companies Economic forecasting Data mining backpropagation algorithms Testing

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A learning method of nonlinear mappings by neural networks with considering their derivatives

A learning method of nonlinear mappings by neural networks w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Y. Kuroe Y. Nakai T. Mori Department of Electronics and Information Science Kyoto Institute of Technology Kyoto Japan

This paper discusses a learning method of neural networks for realizing nonlinear mappings with their smoothness on the networks. We proposed an efficient learning method such that neural networks represent not only input-output relations of nonlinear mappings but also their derivatives for arbitrary connected neural networks. The proposed method makes it possible to train a neural network such that the network approximates a nonlinear mapping and its derivative more accurately.

关键词： Learning systems Neural networks Artificial neural networks Jacobian matrices Ear backpropagation algorithms Robot kinematics Orbital robotics Information science Paper technology

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Grouping connected components using minimal path techniques. Application to reconstruction of vessels in 2D and 3D images

Grouping connected components using minimal path techniques....

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： L.D. Cohen T. Deschamps CEREMADE UMR CNRS 7534 Université de Paris IX Dauphine Paris France Medical Imaging Systems Group Philips Research France Suresnes France

ISBN: (纸本)0769512720

We address the problem of finding a set of contour curves in a 2D or 3D image. We consider the problem of perceptual grouping and contour completion, where the data is an unstructured set of regions in the image. A new method to find complete curves from a set of edge points is presented. Contours, are found as minimal paths between connected components, using the fast marching algorithm. We find the minimal paths between each of these components, until the complete set of these "regions" is connected. The paths are obtained using backpropagation from the saddle points to both components. We then extend this technique to 3D. The data is a set of connected components in a 3D image. We find 3D minimal paths that link together these components. Using a potential based on vessel detection, we illustrate the capability of our approach to reconstruct tree structures in a 3D medical image dataset.

关键词： Image reconstruction Object detection Biomedical imaging Active contours backpropagation algorithms Tree data structures Level set Topology Shape Humans

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Distributed learning and cooperative learning

Distributed learning and cooperative learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Y. Tsukamoto A. Namatame Dept. of Comput. Sci. Nat. Defense Acad. Kanagawa Japan Department of Computer Science National Defense Academy Yokosuka Kanagawa Japan

The concept of modularization and coupling connectionist network modules is a promising way of building large-scale neural networks and improving the learning performance of these networks. On the other hand, the modularization scheme would be of little use if there does not exist such an appropriate learning procedure to train high-level modules separately and to integrate those functionally pre-specified modules efficiently. This paper describes a way of realizing such a learning procedure that supports the modularization and coupling connectionist network modules. We present a distributed learning procedure for networks composed of many separate modular networks, each of which is trained to handle a subset of the complete set of training examples. This paper also describes a way of coupling decomposed neural networks modules. It is shown that with the proper decomposition, the whole network is composed as the weighted summation of the decomposed network modules.

关键词： Neural networks Large-scale systems backpropagation algorithms Sufficient conditions Computer science Interference Supervised learning Boolean functions Vectors

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Neural networks in specification level software size estimation

Neural networks in specification level software size estimat...

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Annual Hawaii International Conference on System Sciences (HICSS)

作者： J. Hakkarainen P. Laamanen R. Rask Department of Computer Science University of Joensuu Finland

Presents a neural network approach to software size estimation. A multilayer feedforward network is trained using the backpropagation algorithm. The training and testing data consist of randomly generated structured analysis descriptions as input data and corresponding algorithm based size metric values as output data. The size metrics used in the experiments are Albrecht's (1979) function points, Symon's (1988) Mark II function points, and DeMarco's (1982) function bang metric. The experiments indicate that neural networks can learn to calculate software size estimates. In each of the experiments it was found that the results depend on the features of the input data, the metric, and the convergence criteria used. The results also encourage the development of a general input set to represent size-related features of graph-based system descriptions.< >

关键词： Neural networks Intelligent networks Size measurement Software measurement backpropagation algorithms Testing Costs Software systems Computer science Multi-layer neural network

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